code: def sparse_to_dense_grads(grads_and_vars):
      """Convert sparse gradients to dense gradients.
  
      All sparse gradients, which are represented as instances of tf.IndexedSlices,
      are converted to dense Tensors. Dense gradients, which are represents as
      Tensors, are unchanged.
  
      The purpose of this conversion is that for small embeddings, which are used by
      this model, applying dense gradients with the AdamOptimizer is faster than
      applying sparse gradients.
  
      Args
        grads_and_vars: A list of (gradient, variable) tuples. Each gradient can
          be a Tensor or an IndexedSlices. Tensors are unchanged, and IndexedSlices
          are converted to dense Tensors.
      Returns:
        The same list of (gradient, variable) as `grads_and_vars`, except each
        IndexedSlices gradient is converted to a Tensor.
      """
  
      # Calling convert_to_tensor changes IndexedSlices into Tensors, and leaves
      # Tensors unchanged.
      return [(tf.convert_to_tensor(g), v) for g, v in grads_and_vars]
  
file_path: autodist/examples/benchmark/utils/recommendation/neumf_model.py
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import sys
  from six.moves import xrange
  import tensorflow
  from utils.recommendation import constants
  from utils.recommendation import movielens
  from utils.recommendation import ncf_common
  from utils.recommendation import stat_utils
  from utils.logs import mlperf_helper
label_desc: Use image node_id 1 for calling a global function with example usage: sparse_to_dense_grads(grads_and_vars) and returns: unknown